The AlphaGo Zero Cheat Sheet (high-res link below)

AlphaGo Zero Explained In One Diagram

Download the AlphaGo Zero cheat sheet

Update! (2nd December 2019)

I’ve just released a series on MuZero — AlphaZero’s younger and cooler brother. Check it out 👇

How to Build Your Own MuZero Using Python (Part 1/3)

How to Build Your Own MuZero Using Python (Part 2/3)

How to Build Your Own MuZero Using Python (Part 3/3)

Update! (26th January 2018)

I’ve just released a post on how you can build AlphaZero using Python and Keras. Check it out 👇

How to build your own AlphaZero AI using Python and Keras

What’s AlphaZero?

Recently Google DeepMind announced AlphaGo Zero — an extraordinary achievement that has shown how it is possible to train an agent to a superhuman level in the highly complex and challenging domain of Go, ‘tabula rasa’ — that is, from a blank slate, with no human expert play used as training data.

It thrashed the previous reincarnation 100–0, using only 4TPUs instead of 48TPUs and a single neural network instead of two.

The paper that the cheat sheet is based on was published in Nature and is available here. I highly recommend you read it, as it explains in detail how deep learning and Monte Carlo Tree Search are combined to produce a powerful reinforcement learning algorithm.

Hopefully you find the AlphaGo Zero cheat sheet useful — let me know if you find any typos or have questions about anything in the document.

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